Parallel Data Processing in Quantum Machine Learning
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| author | Ramezani, Mehdi Zargar, Sina Asadiyan Bahrampour, Abolfazl Shouraki, Saeed Bagheri Bahrampour, Alireza |
| author_facet | Ramezani, Mehdi Zargar, Sina Asadiyan Bahrampour, Abolfazl Shouraki, Saeed Bagheri Bahrampour, Alireza |
| contents | We propose a Quantum Machine Learning (QML) framework that leverages quantum parallelism to process entire training datasets in a single quantum operation, addressing the computational bottleneck of sequential data processing in both classical and quantum settings. Building on the structural analogy between feature extraction in foundational quantum algorithms and parameter optimization in QML, we embed a standard parameterized quantum circuit into an integrated architecture that encodes all training samples into a quantum superposition and applies classification in parallel. This approach reduces the theoretical complexity of loss function evaluation from $O(N^2)$ in conventional QML training to $O(N)$, where $N$ is the dataset size. Numerical simulations on multiple binary and multi-class classification datasets demonstrate that our method achieves classification accuracies comparable to conventional circuits while offering substantial training time savings. These results highlight the potential of quantum-parallel data processing as a scalable pathway to efficient QML implementations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12006 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parallel Data Processing in Quantum Machine Learning Ramezani, Mehdi Zargar, Sina Asadiyan Bahrampour, Abolfazl Shouraki, Saeed Bagheri Bahrampour, Alireza Quantum Physics We propose a Quantum Machine Learning (QML) framework that leverages quantum parallelism to process entire training datasets in a single quantum operation, addressing the computational bottleneck of sequential data processing in both classical and quantum settings. Building on the structural analogy between feature extraction in foundational quantum algorithms and parameter optimization in QML, we embed a standard parameterized quantum circuit into an integrated architecture that encodes all training samples into a quantum superposition and applies classification in parallel. This approach reduces the theoretical complexity of loss function evaluation from $O(N^2)$ in conventional QML training to $O(N)$, where $N$ is the dataset size. Numerical simulations on multiple binary and multi-class classification datasets demonstrate that our method achieves classification accuracies comparable to conventional circuits while offering substantial training time savings. These results highlight the potential of quantum-parallel data processing as a scalable pathway to efficient QML implementations. |
| title | Parallel Data Processing in Quantum Machine Learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2508.12006 |